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Applications of Regression for Categorical Outcomes Using R

David Melamed | Long Doan
€204.60
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A01=David Melamed
A01=Long Doan
advanced regression techniques in R
Author_David Melamed
Author_Long Doan
Average Marginal Effect
Binary Outcomes
categorical data analysis
Category=JMA
Category=JMB
Category=PBT
Conditional Logistic Regression Model
Conditional Marginal Effects
Count Outcomes
Cross-model Comparisons
Delta Method Standard Error
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Estimated Cut Point
Fixed Effects Logistic Regression
General Linear Model
graphical data visualization
Latent Variable Approach
Limited Dependent Variables
Log Linear Models
Log Odds Coefficients
Lower Log Odds
Min 1Q Median 3Q Max
model fit diagnostics
Multinomial Logistic Regression
NA NA
NaN NaN
Negative Binomial
Nominal Outcomes
OLS Regression
Ordinal Outcomes
Parallel Regression Assumption
Partial Proportional Odds Model
Percent CI
Predicted Probability Plot
quantitative research methods
R programming for social scientists
R Studio
Rank Ordered Logit Model
Regression Models
Safe Female
social science statistics

Product details

  • ISBN 9780367894634
  • Weight: 539g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Jul 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide.

Key Features:

  • Applied- in the sense that we will provide code that others can easily adapt

  • Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work

  • Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource

  • Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book

  • Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results

  • Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.
About David Melamed | Long Doan

David Melamed is a Professor of Sociology and Translational Data Analytics at The Ohio State University. His research interests include the emergence of stratification, cooperation and segregation in dynamical systems, and statistics and methodology. Since 2019 he has been co-Editor of Sociological Methodology.

Long Doan is an Associate Professor of Sociology at the University of Maryland, College Park. His research examines how various social psychological processes like identity, intergroup competition, and bias help to explain the emergence and persistence of social stratification. He focuses on inequalities based on sexuality, gender, and race.

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